A framework for deriving semantic web services

Web service-based development represents an emerging approach for the development of distributed information systems. Web services have been mainly applied by software practitioners as a means to modularize system functionality that can be offered across a network (e.g., intranet and/or the Internet). Although web services have been predominantly developed as a technical solution for integrating software systems, there is a more business-oriented aspect that developers and enterprises need to deal with in order to benefit from the full potential of web services in an electronic market. This ‘ignored’ aspect is the representation of the semantics underlying the services themselves as well as the ‘things’ that the services manage. Currently languages like the Web Services Description Language (WSDL) provide the syntactic means to describe web services, but lack in providing a semantic underpinning. In order to harvest all the benefits of web services technology, a framework has been developed for deriving business semantics from syntactic descriptions of web services. The benefits of such a framework are two-fold. Firstly, the framework provides a way to gradually construct domain ontologies from previously defined technical services. Secondly, the framework enables the migration of syntactically defined web services toward semantic web services. The study follows a design research approach which (1) identifies the problem area and its relevance from an industrial case study and previous research, (2) develops the framework as a design artifact and (3) evaluates the application of the framework through a relevant scenario

Applications and Uses of Dental Ontologies

The development of a number of large-scale semantically-rich ontologies for biomedicine attests to the interest of life science researchers and clinicians in Semantic Web technologies. To date, however, the dental profession has lagged behind other areas of biomedicine in developing a commonly accepted, standardized ontology to support the representation of dental knowledge and information. This paper attempts to identify some of the potential uses of dental ontologies as part of an effort to motivate the development of ontologies for the dental domain. The identified uses of dental ontologies include support for advanced data analysis and knowledge discovery capabilities, the implementation of novel education and training technologies, the development of information exchange and interoperability solutions, the better integration of scientific and clinical evidence into clinical decision-making, and the development of better clinical decision support systems. Some of the social issues raised by these uses include the ethics of using patient data without consent, the role played by ontologies in enforcing compliance with regulatory criteria and legislative constraints, and the extent to which the advent of the Semantic Web introduces new training requirements for dental students. Some of the technological issues relate to the need to extract information from a variety of resources (for example, natural language texts), the need to automatically annotate information resources with ontology elements, and the need to establish mappings between a variety of existing dental terminologies

How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed

The Semantic Web Revisited

The original Scientific American article on the Semantic Web appeared in 2001. It described the evolution of a Web that consisted largely of documents for humans to read to one that included data and information for computers to manipulate. The Semantic Web is a Web of actionable information--information derived from data through a semantic theory for interpreting the symbols.This simple idea, however, remains largely unrealized. Shopbots and auction bots abound on the Web, but these are essentially handcrafted for particular tasks; they have little ability to interact with heterogeneous data and information types. Because we haven't yet delivered large-scale, agent-based mediation, some commentators argue that the Semantic Web has failed to deliver. We argue that agents can only flourish when standards are well established and that the Web standards for expressing shared meaning have progressed steadily over the past five years. Furthermore, we see the use of ontologies in the e-science community presaging ultimate success for the Semantic Web--just as the use of HTTP within the CERN particle physics community led to the revolutionary success of the original Web. This article is part of a special issue on the Future of AI

Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea